Overview

Dataset statistics

Number of variables27
Number of observations167375
Missing cells0
Missing cells (%)0.0%
Duplicate rows1525
Duplicate rows (%)0.9%
Total size in memory34.5 MiB
Average record size in memory216.0 B

Variable types

Unsupported2
Categorical5
Numeric20

Alerts

accident_year has constant value "2020" Constant
Dataset has 1525 (0.9%) duplicate rowsDuplicates
generic_make_model has a high cardinality: 692 distinct values High cardinality
vehicle_location_restricted_lane is highly correlated with skidding_and_overturning and 4 other fieldsHigh correlation
skidding_and_overturning is highly correlated with vehicle_location_restricted_lane and 3 other fieldsHigh correlation
hit_object_in_carriageway is highly correlated with vehicle_location_restricted_lane and 4 other fieldsHigh correlation
vehicle_leaving_carriageway is highly correlated with vehicle_location_restricted_lane and 3 other fieldsHigh correlation
hit_object_off_carriageway is highly correlated with vehicle_location_restricted_lane and 4 other fieldsHigh correlation
vehicle_left_hand_drive is highly correlated with vehicle_location_restricted_lane and 2 other fieldsHigh correlation
age_of_driver is highly correlated with age_band_of_driverHigh correlation
age_band_of_driver is highly correlated with age_of_driverHigh correlation
engine_capacity_cc is highly correlated with propulsion_code and 1 other fieldsHigh correlation
propulsion_code is highly correlated with engine_capacity_cc and 1 other fieldsHigh correlation
age_of_vehicle is highly correlated with engine_capacity_cc and 1 other fieldsHigh correlation
driver_imd_decile is highly correlated with driver_home_area_typeHigh correlation
driver_home_area_type is highly correlated with driver_imd_decileHigh correlation
vehicle_manoeuvre is highly correlated with vehicle_direction_from and 7 other fieldsHigh correlation
vehicle_direction_from is highly correlated with vehicle_manoeuvreHigh correlation
vehicle_direction_to is highly correlated with vehicle_manoeuvreHigh correlation
vehicle_location_restricted_lane is highly correlated with vehicle_manoeuvre and 5 other fieldsHigh correlation
skidding_and_overturning is highly correlated with vehicle_manoeuvre and 5 other fieldsHigh correlation
hit_object_in_carriageway is highly correlated with vehicle_manoeuvre and 5 other fieldsHigh correlation
vehicle_leaving_carriageway is highly correlated with vehicle_manoeuvre and 5 other fieldsHigh correlation
hit_object_off_carriageway is highly correlated with vehicle_manoeuvre and 5 other fieldsHigh correlation
vehicle_left_hand_drive is highly correlated with vehicle_manoeuvre and 5 other fieldsHigh correlation
sex_of_driver is highly correlated with age_band_of_driverHigh correlation
age_of_driver is highly correlated with age_band_of_driverHigh correlation
age_band_of_driver is highly correlated with sex_of_driver and 2 other fieldsHigh correlation
driver_imd_decile is highly correlated with driver_home_area_typeHigh correlation
driver_home_area_type is highly correlated with age_band_of_driver and 1 other fieldsHigh correlation
vehicle_location_restricted_lane is highly correlated with skidding_and_overturning and 4 other fieldsHigh correlation
skidding_and_overturning is highly correlated with vehicle_location_restricted_lane and 3 other fieldsHigh correlation
hit_object_in_carriageway is highly correlated with vehicle_location_restricted_lane and 3 other fieldsHigh correlation
vehicle_leaving_carriageway is highly correlated with vehicle_location_restricted_lane and 3 other fieldsHigh correlation
hit_object_off_carriageway is highly correlated with vehicle_location_restricted_lane and 3 other fieldsHigh correlation
vehicle_left_hand_drive is highly correlated with vehicle_location_restricted_laneHigh correlation
age_of_driver is highly correlated with age_band_of_driverHigh correlation
age_band_of_driver is highly correlated with age_of_driverHigh correlation
engine_capacity_cc is highly correlated with propulsion_codeHigh correlation
propulsion_code is highly correlated with engine_capacity_ccHigh correlation
driver_imd_decile is highly correlated with driver_home_area_typeHigh correlation
driver_home_area_type is highly correlated with driver_imd_decileHigh correlation
sex_of_driver is highly correlated with accident_yearHigh correlation
driver_home_area_type is highly correlated with accident_yearHigh correlation
accident_year is highly correlated with sex_of_driver and 2 other fieldsHigh correlation
vehicle_left_hand_drive is highly correlated with accident_yearHigh correlation
vehicle_type is highly correlated with towing_and_articulation and 1 other fieldsHigh correlation
towing_and_articulation is highly correlated with vehicle_type and 2 other fieldsHigh correlation
vehicle_manoeuvre is highly correlated with vehicle_direction_from and 9 other fieldsHigh correlation
vehicle_direction_from is highly correlated with vehicle_manoeuvre and 9 other fieldsHigh correlation
vehicle_direction_to is highly correlated with vehicle_manoeuvre and 9 other fieldsHigh correlation
vehicle_location_restricted_lane is highly correlated with vehicle_manoeuvre and 9 other fieldsHigh correlation
junction_location is highly correlated with vehicle_manoeuvre and 9 other fieldsHigh correlation
skidding_and_overturning is highly correlated with towing_and_articulation and 10 other fieldsHigh correlation
hit_object_in_carriageway is highly correlated with vehicle_manoeuvre and 9 other fieldsHigh correlation
vehicle_leaving_carriageway is highly correlated with towing_and_articulation and 10 other fieldsHigh correlation
hit_object_off_carriageway is highly correlated with vehicle_manoeuvre and 9 other fieldsHigh correlation
first_point_of_impact is highly correlated with vehicle_manoeuvre and 8 other fieldsHigh correlation
vehicle_left_hand_drive is highly correlated with vehicle_manoeuvre and 8 other fieldsHigh correlation
sex_of_driver is highly correlated with age_of_driver and 3 other fieldsHigh correlation
age_of_driver is highly correlated with sex_of_driver and 3 other fieldsHigh correlation
age_band_of_driver is highly correlated with sex_of_driver and 3 other fieldsHigh correlation
engine_capacity_cc is highly correlated with vehicle_typeHigh correlation
driver_imd_decile is highly correlated with sex_of_driver and 3 other fieldsHigh correlation
driver_home_area_type is highly correlated with sex_of_driver and 3 other fieldsHigh correlation
vehicle_reference is highly skewed (γ1 = 362.6319019) Skewed
accident_index is an unsupported type, check if it needs cleaning or further analysis Unsupported
accident_reference is an unsupported type, check if it needs cleaning or further analysis Unsupported
towing_and_articulation has 160772 (96.1%) zeros Zeros
vehicle_direction_from has 7253 (4.3%) zeros Zeros
vehicle_direction_to has 7002 (4.2%) zeros Zeros
vehicle_location_restricted_lane has 148988 (89.0%) zeros Zeros
junction_location has 67731 (40.5%) zeros Zeros
skidding_and_overturning has 136377 (81.5%) zeros Zeros
hit_object_in_carriageway has 145392 (86.9%) zeros Zeros
vehicle_leaving_carriageway has 136659 (81.6%) zeros Zeros
hit_object_off_carriageway has 144089 (86.1%) zeros Zeros
first_point_of_impact has 9462 (5.7%) zeros Zeros
age_of_vehicle has 4000 (2.4%) zeros Zeros

Reproduction

Analysis started2022-02-22 14:08:40.114775
Analysis finished2022-02-22 14:10:21.687377
Duration1 minute and 41.57 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

accident_index
Unsupported

REJECTED
UNSUPPORTED

Missing0
Missing (%)0.0%
Memory size1.3 MiB

accident_year
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
2020
167375 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020
2nd row2020
3rd row2020
4th row2020
5th row2020

Common Values

ValueCountFrequency (%)
2020167375
100.0%

Length

2022-02-22T15:10:21.764013image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-22T15:10:21.832075image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
2020167375
100.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

accident_reference
Unsupported

REJECTED
UNSUPPORTED

Missing0
Missing (%)0.0%
Memory size1.3 MiB

vehicle_reference
Real number (ℝ≥0)

SKEWED

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.549197909
Minimum1
Maximum999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2022-02-22T15:10:21.905881image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile3
Maximum999
Range998
Interquartile range (IQR)1

Descriptive statistics

Standard deviation2.53819583
Coefficient of variation (CV)1.63839353
Kurtosis142489.9006
Mean1.549197909
Median Absolute Deviation (MAD)0
Skewness362.6319019
Sum259297
Variance6.442438072
MonotonicityNot monotonic
2022-02-22T15:10:22.062471image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
191152
54.5%
265477
39.1%
38106
 
4.8%
41842
 
1.1%
5503
 
0.3%
6171
 
0.1%
772
 
< 0.1%
825
 
< 0.1%
911
 
< 0.1%
107
 
< 0.1%
Other values (4)9
 
< 0.1%
ValueCountFrequency (%)
191152
54.5%
265477
39.1%
38106
 
4.8%
41842
 
1.1%
5503
 
0.3%
6171
 
0.1%
772
 
< 0.1%
825
 
< 0.1%
911
 
< 0.1%
107
 
< 0.1%
ValueCountFrequency (%)
9991
 
< 0.1%
131
 
< 0.1%
122
 
< 0.1%
115
 
< 0.1%
107
 
< 0.1%
911
 
< 0.1%
825
 
< 0.1%
772
 
< 0.1%
6171
 
0.1%
5503
0.3%

vehicle_type
Real number (ℝ≥0)

HIGH CORRELATION

Distinct20
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.05130993
Minimum1
Maximum98
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2022-02-22T15:10:22.254562image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q19
median9
Q39
95-th percentile19
Maximum98
Range97
Interquartile range (IQR)0

Descriptive statistics

Standard deviation11.69241242
Coefficient of variation (CV)1.163272499
Kurtosis41.79629716
Mean10.05130993
Median Absolute Deviation (MAD)0
Skewness6.147087048
Sum1682338
Variance136.7125082
MonotonicityNot monotonic
2022-02-22T15:10:22.420736image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
9114145
68.2%
116766
 
10.0%
1910338
 
6.2%
37523
 
4.5%
53784
 
2.3%
82612
 
1.6%
212501
 
1.5%
112213
 
1.3%
41691
 
1.0%
901185
 
0.7%
Other values (10)4617
 
2.8%
ValueCountFrequency (%)
116766
 
10.0%
21128
 
0.7%
37523
 
4.5%
41691
 
1.0%
53784
 
2.3%
82612
 
1.6%
9114145
68.2%
10225
 
0.1%
112213
 
1.3%
1690
 
0.1%
ValueCountFrequency (%)
981072
 
0.6%
97478
 
0.3%
901185
 
0.7%
2393
 
0.1%
22189
 
0.1%
212501
 
1.5%
20898
 
0.5%
1910338
6.2%
189
 
< 0.1%
17435
 
0.3%

towing_and_articulation
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2216639283
Minimum-1
Maximum9
Zeros160772
Zeros (%)96.1%
Negative689
Negative (%)0.4%
Memory size1.3 MiB
2022-02-22T15:10:22.544669image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum9
Range10
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.344229731
Coefficient of variation (CV)6.064269188
Kurtosis36.58231716
Mean0.2216639283
Median Absolute Deviation (MAD)0
Skewness6.136178045
Sum37101
Variance1.806953569
MonotonicityNot monotonic
2022-02-22T15:10:22.647949image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0160772
96.1%
93598
 
2.1%
11324
 
0.8%
-1689
 
0.4%
4645
 
0.4%
5242
 
0.1%
384
 
0.1%
221
 
< 0.1%
ValueCountFrequency (%)
-1689
 
0.4%
0160772
96.1%
11324
 
0.8%
221
 
< 0.1%
384
 
0.1%
4645
 
0.4%
5242
 
0.1%
93598
 
2.1%
ValueCountFrequency (%)
93598
 
2.1%
5242
 
0.1%
4645
 
0.4%
384
 
0.1%
221
 
< 0.1%
11324
 
0.8%
0160772
96.1%
-1689
 
0.4%

vehicle_manoeuvre
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION

Distinct20
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.5389873
Minimum-1
Maximum99
Zeros0
Zeros (%)0.0%
Negative679
Negative (%)0.4%
Memory size1.3 MiB
2022-02-22T15:10:22.752784image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile2
Q19
median18
Q318
95-th percentile99
Maximum99
Range100
Interquartile range (IQR)9

Descriptive statistics

Standard deviation23.66127375
Coefficient of variation (CV)1.210977488
Kurtosis6.816089267
Mean19.5389873
Median Absolute Deviation (MAD)2
Skewness2.823442978
Sum3270338
Variance559.8558757
MonotonicityNot monotonic
2022-02-22T15:10:22.983258image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
1878191
46.7%
914799
 
8.8%
9912845
 
7.7%
49040
 
5.4%
57467
 
4.5%
27422
 
4.4%
36872
 
4.1%
75568
 
3.3%
175459
 
3.3%
164740
 
2.8%
Other values (10)14972
 
8.9%
ValueCountFrequency (%)
-1679
 
0.4%
12091
 
1.2%
27422
4.4%
36872
4.1%
49040
5.4%
57467
4.5%
61199
 
0.7%
75568
 
3.3%
8736
 
0.4%
914799
8.8%
ValueCountFrequency (%)
9912845
 
7.7%
1878191
46.7%
175459
 
3.3%
164740
 
2.8%
151140
 
0.7%
141409
 
0.8%
133097
 
1.9%
121262
 
0.8%
111165
 
0.7%
102194
 
1.3%

vehicle_direction_from
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.406386856
Minimum-1
Maximum9
Zeros7253
Zeros (%)4.3%
Negative1640
Negative (%)1.0%
Memory size1.3 MiB
2022-02-22T15:10:23.091473image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile0
Q12
median5
Q37
95-th percentile9
Maximum9
Range10
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.718825873
Coefficient of variation (CV)0.6170193317
Kurtosis-1.128623764
Mean4.406386856
Median Absolute Deviation (MAD)2
Skewness0.04333357971
Sum737519
Variance7.392014127
MonotonicityNot monotonic
2022-02-22T15:10:23.181280image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
125605
15.3%
523722
14.2%
322001
13.1%
721339
12.7%
213621
8.1%
613492
8.1%
912978
7.8%
812919
7.7%
412805
7.7%
07253
 
4.3%
ValueCountFrequency (%)
-11640
 
1.0%
07253
 
4.3%
125605
15.3%
213621
8.1%
322001
13.1%
412805
7.7%
523722
14.2%
613492
8.1%
721339
12.7%
812919
7.7%
ValueCountFrequency (%)
912978
7.8%
812919
7.7%
721339
12.7%
613492
8.1%
523722
14.2%
412805
7.7%
322001
13.1%
213621
8.1%
125605
15.3%
07253
 
4.3%

vehicle_direction_to
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.455067961
Minimum-1
Maximum9
Zeros7002
Zeros (%)4.2%
Negative1653
Negative (%)1.0%
Memory size1.3 MiB
2022-02-22T15:10:23.275232image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile0
Q12
median5
Q37
95-th percentile9
Maximum9
Range10
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.69693723
Coefficient of variation (CV)0.6053638808
Kurtosis-1.104588061
Mean4.455067961
Median Absolute Deviation (MAD)2
Skewness0.02183138814
Sum745667
Variance7.273470423
MonotonicityNot monotonic
2022-02-22T15:10:23.369408image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
524192
14.5%
123537
14.1%
321901
13.1%
721870
13.1%
214379
8.6%
613525
8.1%
413346
8.0%
812992
7.8%
912978
7.8%
07002
 
4.2%
ValueCountFrequency (%)
-11653
 
1.0%
07002
 
4.2%
123537
14.1%
214379
8.6%
321901
13.1%
413346
8.0%
524192
14.5%
613525
8.1%
721870
13.1%
812992
7.8%
ValueCountFrequency (%)
912978
7.8%
812992
7.8%
721870
13.1%
613525
8.1%
524192
14.5%
413346
8.0%
321901
13.1%
214379
8.6%
123537
14.1%
07002
 
4.2%

vehicle_location_restricted_lane
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.537870052
Minimum-1
Maximum99
Zeros148988
Zeros (%)89.0%
Negative640
Negative (%)0.4%
Memory size1.3 MiB
2022-02-22T15:10:23.462458image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile0
Q10
median0
Q30
95-th percentile99
Maximum99
Range100
Interquartile range (IQR)0

Descriptive statistics

Standard deviation25.89839334
Coefficient of variation (CV)3.435770737
Kurtosis8.528134831
Mean7.537870052
Median Absolute Deviation (MAD)0
Skewness3.240266131
Sum1261651
Variance670.7267778
MonotonicityNot monotonic
2022-02-22T15:10:23.560380image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0148988
89.0%
9912397
 
7.4%
92527
 
1.5%
4715
 
0.4%
2687
 
0.4%
-1640
 
0.4%
6575
 
0.3%
5316
 
0.2%
8233
 
0.1%
1119
 
0.1%
Other values (2)178
 
0.1%
ValueCountFrequency (%)
-1640
 
0.4%
0148988
89.0%
1119
 
0.1%
2687
 
0.4%
362
 
< 0.1%
4715
 
0.4%
5316
 
0.2%
6575
 
0.3%
7116
 
0.1%
8233
 
0.1%
ValueCountFrequency (%)
9912397
7.4%
92527
 
1.5%
8233
 
0.1%
7116
 
0.1%
6575
 
0.3%
5316
 
0.2%
4715
 
0.4%
362
 
< 0.1%
2687
 
0.4%
1119
 
0.1%

junction_location
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.598303211
Minimum-1
Maximum9
Zeros67731
Zeros (%)40.5%
Negative284
Negative (%)0.2%
Memory size1.3 MiB
2022-02-22T15:10:23.656285image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile0
Q10
median1
Q36
95-th percentile9
Maximum9
Range10
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.285627255
Coefficient of variation (CV)1.264528035
Kurtosis-0.8421346062
Mean2.598303211
Median Absolute Deviation (MAD)1
Skewness0.9276256088
Sum434891
Variance10.79534646
MonotonicityNot monotonic
2022-02-22T15:10:23.747640image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
067731
40.5%
136422
21.8%
825526
 
15.3%
99373
 
5.6%
29059
 
5.4%
66750
 
4.0%
45154
 
3.1%
53847
 
2.3%
32721
 
1.6%
7508
 
0.3%
ValueCountFrequency (%)
-1284
 
0.2%
067731
40.5%
136422
21.8%
29059
 
5.4%
32721
 
1.6%
45154
 
3.1%
53847
 
2.3%
66750
 
4.0%
7508
 
0.3%
825526
 
15.3%
ValueCountFrequency (%)
99373
 
5.6%
825526
 
15.3%
7508
 
0.3%
66750
 
4.0%
53847
 
2.3%
45154
 
3.1%
32721
 
1.6%
29059
 
5.4%
136422
21.8%
067731
40.5%

skidding_and_overturning
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.920334578
Minimum-1
Maximum9
Zeros136377
Zeros (%)81.5%
Negative662
Negative (%)0.4%
Memory size1.3 MiB
2022-02-22T15:10:23.841780image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile0
Q10
median0
Q30
95-th percentile9
Maximum9
Range10
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.49721216
Coefficient of variation (CV)2.713374266
Kurtosis5.73521314
Mean0.920334578
Median Absolute Deviation (MAD)0
Skewness2.708486534
Sum154041
Variance6.236068573
MonotonicityNot monotonic
2022-02-22T15:10:23.931038image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0136377
81.5%
913038
 
7.8%
110096
 
6.0%
54254
 
2.5%
22875
 
1.7%
-1662
 
0.4%
347
 
< 0.1%
426
 
< 0.1%
ValueCountFrequency (%)
-1662
 
0.4%
0136377
81.5%
110096
 
6.0%
22875
 
1.7%
347
 
< 0.1%
426
 
< 0.1%
54254
 
2.5%
913038
 
7.8%
ValueCountFrequency (%)
913038
 
7.8%
54254
 
2.5%
426
 
< 0.1%
347
 
< 0.1%
22875
 
1.7%
110096
 
6.0%
0136377
81.5%
-1662
 
0.4%

hit_object_in_carriageway
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.859979089
Minimum-1
Maximum99
Zeros145392
Zeros (%)86.9%
Negative636
Negative (%)0.4%
Memory size1.3 MiB
2022-02-22T15:10:24.026265image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile0
Q10
median0
Q30
95-th percentile99
Maximum99
Range100
Interquartile range (IQR)0

Descriptive statistics

Standard deviation26.13339657
Coefficient of variation (CV)3.32486846
Kurtosis8.200251291
Mean7.859979089
Median Absolute Deviation (MAD)0
Skewness3.185586887
Sum1315564
Variance682.9544163
MonotonicityNot monotonic
2022-02-22T15:10:24.133628image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0145392
86.9%
9912664
 
7.6%
43252
 
1.9%
102605
 
1.6%
7942
 
0.6%
11813
 
0.5%
-1636
 
0.4%
8261
 
0.2%
12220
 
0.1%
9220
 
0.1%
Other values (4)370
 
0.2%
ValueCountFrequency (%)
-1636
 
0.4%
0145392
86.9%
1109
 
0.1%
2125
 
0.1%
43252
 
1.9%
514
 
< 0.1%
6122
 
0.1%
7942
 
0.6%
8261
 
0.2%
9220
 
0.1%
ValueCountFrequency (%)
9912664
7.6%
12220
 
0.1%
11813
 
0.5%
102605
 
1.6%
9220
 
0.1%
8261
 
0.2%
7942
 
0.6%
6122
 
0.1%
514
 
< 0.1%
43252
 
1.9%

vehicle_leaving_carriageway
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.005078417
Minimum-1
Maximum9
Zeros136659
Zeros (%)81.6%
Negative644
Negative (%)0.4%
Memory size1.3 MiB
2022-02-22T15:10:24.230932image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile0
Q10
median0
Q30
95-th percentile9
Maximum9
Range10
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.627550463
Coefficient of variation (CV)2.614274089
Kurtosis4.387558948
Mean1.005078417
Median Absolute Deviation (MAD)0
Skewness2.476679208
Sum168225
Variance6.904021433
MonotonicityNot monotonic
2022-02-22T15:10:24.327737image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0136659
81.6%
912493
 
7.5%
19294
 
5.6%
74679
 
2.8%
21133
 
0.7%
3848
 
0.5%
-1644
 
0.4%
8614
 
0.4%
4534
 
0.3%
5335
 
0.2%
ValueCountFrequency (%)
-1644
 
0.4%
0136659
81.6%
19294
 
5.6%
21133
 
0.7%
3848
 
0.5%
4534
 
0.3%
5335
 
0.2%
6142
 
0.1%
74679
 
2.8%
8614
 
0.4%
ValueCountFrequency (%)
912493
 
7.5%
8614
 
0.4%
74679
 
2.8%
6142
 
0.1%
5335
 
0.2%
4534
 
0.3%
3848
 
0.5%
21133
 
0.7%
19294
 
5.6%
0136659
81.6%

hit_object_off_carriageway
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.986999253
Minimum-1
Maximum99
Zeros144089
Zeros (%)86.1%
Negative4
Negative (%)< 0.1%
Memory size1.3 MiB
2022-02-22T15:10:24.426873image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile0
Q10
median0
Q30
95-th percentile99
Maximum99
Range100
Interquartile range (IQR)0

Descriptive statistics

Standard deviation26.2386465
Coefficient of variation (CV)3.28516952
Kurtosis8.063658341
Mean7.986999253
Median Absolute Deviation (MAD)0
Skewness3.162903526
Sum1336824
Variance688.4665701
MonotonicityNot monotonic
2022-02-22T15:10:24.526814image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0144089
86.1%
9912783
 
7.6%
112229
 
1.3%
41937
 
1.2%
101813
 
1.1%
11026
 
0.6%
9952
 
0.6%
2883
 
0.5%
7658
 
0.4%
6584
 
0.3%
Other values (4)421
 
0.3%
ValueCountFrequency (%)
-14
 
< 0.1%
0144089
86.1%
11026
 
0.6%
2883
 
0.5%
3340
 
0.2%
41937
 
1.2%
564
 
< 0.1%
6584
 
0.3%
7658
 
0.4%
813
 
< 0.1%
ValueCountFrequency (%)
9912783
7.6%
112229
 
1.3%
101813
 
1.1%
9952
 
0.6%
813
 
< 0.1%
7658
 
0.4%
6584
 
0.3%
564
 
< 0.1%
41937
 
1.2%
3340
 
0.2%

first_point_of_impact
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.993236744
Minimum-1
Maximum9
Zeros9462
Zeros (%)5.7%
Negative939
Negative (%)0.6%
Memory size1.3 MiB
2022-02-22T15:10:24.657538image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile0
Q11
median1
Q33
95-th percentile4
Maximum9
Range10
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.750087857
Coefficient of variation (CV)0.8780130423
Kurtosis6.218340897
Mean1.993236744
Median Absolute Deviation (MAD)1
Skewness2.204005552
Sum333618
Variance3.062807509
MonotonicityNot monotonic
2022-02-22T15:10:24.776624image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
182297
49.2%
226346
 
15.7%
322421
 
13.4%
420177
 
12.1%
09462
 
5.7%
95733
 
3.4%
-1939
 
0.6%
ValueCountFrequency (%)
-1939
 
0.6%
09462
 
5.7%
182297
49.2%
226346
 
15.7%
322421
 
13.4%
420177
 
12.1%
95733
 
3.4%
ValueCountFrequency (%)
95733
 
3.4%
420177
 
12.1%
322421
 
13.4%
226346
 
15.7%
182297
49.2%
09462
 
5.7%
-1939
 
0.6%

vehicle_left_hand_drive
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
1
155707 
9
 
9444
2
 
1347
-1
 
877

Length

Max length2
Median length1
Mean length1.005239731
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1155707
93.0%
99444
 
5.6%
21347
 
0.8%
-1877
 
0.5%

Length

2022-02-22T15:10:24.924575image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-22T15:10:25.018574image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
1156584
93.6%
99444
 
5.6%
21347
 
0.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

journey_purpose_of_driver
Real number (ℝ)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.717066468
Minimum-1
Maximum6
Zeros0
Zeros (%)0.0%
Negative184
Negative (%)0.1%
Memory size1.3 MiB
2022-02-22T15:10:25.109948image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile1
Q14
median6
Q36
95-th percentile6
Maximum6
Range7
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.914970801
Coefficient of variation (CV)0.4059664655
Kurtosis-0.4672557474
Mean4.717066468
Median Absolute Deviation (MAD)0
Skewness-1.132316708
Sum789519
Variance3.66711317
MonotonicityNot monotonic
2022-02-22T15:10:25.221718image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
699151
59.2%
526104
 
15.6%
122408
 
13.4%
217309
 
10.3%
31625
 
1.0%
4594
 
0.4%
-1184
 
0.1%
ValueCountFrequency (%)
-1184
 
0.1%
122408
 
13.4%
217309
 
10.3%
31625
 
1.0%
4594
 
0.4%
526104
 
15.6%
699151
59.2%
ValueCountFrequency (%)
699151
59.2%
526104
 
15.6%
4594
 
0.4%
31625
 
1.0%
217309
 
10.3%
122408
 
13.4%
-1184
 
0.1%

sex_of_driver
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
1
106240 
2
41820 
3
19302 
-1
 
13

Length

Max length2
Median length1
Mean length1.00007767
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row1
3rd row3
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1106240
63.5%
241820
 
25.0%
319302
 
11.5%
-113
 
< 0.1%

Length

2022-02-22T15:10:25.341509image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-22T15:10:25.572922image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
1106253
63.5%
241820
 
25.0%
319302
 
11.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

age_of_driver
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct99
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.50965795
Minimum-1
Maximum100
Zeros0
Zeros (%)0.0%
Negative23344
Negative (%)13.9%
Memory size1.3 MiB
2022-02-22T15:10:25.809758image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q122
median34
Q349
95-th percentile69
Maximum100
Range101
Interquartile range (IQR)27

Descriptive statistics

Standard deviation20.79751733
Coefficient of variation (CV)0.6026578807
Kurtosis-0.392178558
Mean34.50965795
Median Absolute Deviation (MAD)13
Skewness0.01386833901
Sum5776054
Variance432.536727
MonotonicityNot monotonic
2022-02-22T15:10:26.137706image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-123344
 
13.9%
305308
 
3.2%
293672
 
2.2%
283622
 
2.2%
253599
 
2.2%
273514
 
2.1%
323471
 
2.1%
263468
 
2.1%
313386
 
2.0%
333382
 
2.0%
Other values (89)110609
66.1%
ValueCountFrequency (%)
-123344
13.9%
32
 
< 0.1%
410
 
< 0.1%
522
 
< 0.1%
639
 
< 0.1%
746
 
< 0.1%
846
 
< 0.1%
970
 
< 0.1%
10100
 
0.1%
11187
 
0.1%
ValueCountFrequency (%)
1001
 
< 0.1%
992
 
< 0.1%
983
 
< 0.1%
973
 
< 0.1%
969
 
< 0.1%
9510
 
< 0.1%
9414
 
< 0.1%
9334
< 0.1%
9248
< 0.1%
9155
< 0.1%

age_band_of_driver
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.780271845
Minimum-1
Maximum11
Zeros0
Zeros (%)0.0%
Negative23344
Negative (%)13.9%
Memory size1.3 MiB
2022-02-22T15:10:26.313725image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q15
median6
Q38
95-th percentile10
Maximum11
Range12
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.185713259
Coefficient of variation (CV)0.5511355426
Kurtosis0.3274912128
Mean5.780271845
Median Absolute Deviation (MAD)2
Skewness-1.003102528
Sum967473
Variance10.14876897
MonotonicityNot monotonic
2022-02-22T15:10:26.437582image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
636494
21.8%
727108
16.2%
824008
14.3%
-123344
13.9%
516695
10.0%
915562
9.3%
411046
 
6.6%
106941
 
4.1%
114343
 
2.6%
31499
 
0.9%
Other values (2)335
 
0.2%
ValueCountFrequency (%)
-123344
13.9%
134
 
< 0.1%
2301
 
0.2%
31499
 
0.9%
411046
 
6.6%
516695
10.0%
636494
21.8%
727108
16.2%
824008
14.3%
915562
9.3%
ValueCountFrequency (%)
114343
 
2.6%
106941
 
4.1%
915562
9.3%
824008
14.3%
727108
16.2%
636494
21.8%
516695
10.0%
411046
 
6.6%
31499
 
0.9%
2301
 
0.2%

engine_capacity_cc
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct985
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1332.294554
Minimum-1
Maximum17696
Zeros0
Zeros (%)0.0%
Negative43603
Negative (%)26.1%
Memory size1.3 MiB
2022-02-22T15:10:26.577538image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q1-1
median1368
Q31910
95-th percentile2987
Maximum17696
Range17697
Interquartile range (IQR)1911

Descriptive statistics

Standard deviation1543.509942
Coefficient of variation (CV)1.158535053
Kurtosis26.12224291
Mean1332.294554
Median Absolute Deviation (MAD)627
Skewness4.109213084
Sum222992801
Variance2382422.942
MonotonicityNot monotonic
2022-02-22T15:10:26.738817image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-143603
26.1%
15989171
 
5.5%
19955440
 
3.3%
19685292
 
3.2%
9984830
 
2.9%
1254825
 
2.9%
15603784
 
2.3%
12423362
 
2.0%
21432835
 
1.7%
19972812
 
1.7%
Other values (975)81421
48.6%
ValueCountFrequency (%)
-143603
26.1%
71
 
< 0.1%
421
 
< 0.1%
4816
 
< 0.1%
49449
 
0.3%
50101
 
0.1%
792
 
< 0.1%
8511
 
< 0.1%
892
 
< 0.1%
963
 
< 0.1%
ValueCountFrequency (%)
176961
 
< 0.1%
1640017
< 0.1%
1635316
< 0.1%
163501
 
< 0.1%
161232
 
< 0.1%
161201
 
< 0.1%
160005
 
< 0.1%
159281
 
< 0.1%
156079
< 0.1%
152562
 
< 0.1%

propulsion_code
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.9701209858
Minimum-1
Maximum12
Zeros0
Zeros (%)0.0%
Negative43015
Negative (%)25.7%
Memory size1.3 MiB
2022-02-22T15:10:26.874783image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q1-1
median1
Q32
95-th percentile2
Maximum12
Range13
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.585309874
Coefficient of variation (CV)1.634136255
Kurtosis7.256120777
Mean0.9701209858
Median Absolute Deviation (MAD)1
Skewness1.6993152
Sum162374
Variance2.513207398
MonotonicityNot monotonic
2022-02-22T15:10:27.000446image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
167383
40.3%
252598
31.4%
-143015
25.7%
83765
 
2.2%
3459
 
0.3%
764
 
< 0.1%
1256
 
< 0.1%
518
 
< 0.1%
615
 
< 0.1%
41
 
< 0.1%
ValueCountFrequency (%)
-143015
25.7%
167383
40.3%
252598
31.4%
3459
 
0.3%
41
 
< 0.1%
518
 
< 0.1%
615
 
< 0.1%
764
 
< 0.1%
83765
 
2.2%
91
 
< 0.1%
ValueCountFrequency (%)
1256
 
< 0.1%
91
 
< 0.1%
83765
 
2.2%
764
 
< 0.1%
615
 
< 0.1%
518
 
< 0.1%
41
 
< 0.1%
3459
 
0.3%
252598
31.4%
167383
40.3%

age_of_vehicle
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct75
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.820600448
Minimum-1
Maximum96
Zeros4000
Zeros (%)2.4%
Negative43072
Negative (%)25.7%
Memory size1.3 MiB
2022-02-22T15:10:27.129109image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q1-1
median5
Q311
95-th percentile17
Maximum96
Range97
Interquartile range (IQR)12

Descriptive statistics

Standard deviation6.331929007
Coefficient of variation (CV)1.087848077
Kurtosis3.021169788
Mean5.820600448
Median Absolute Deviation (MAD)6
Skewness0.9891865746
Sum974223
Variance40.09332495
MonotonicityNot monotonic
2022-02-22T15:10:27.278389image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-143072
25.7%
19310
 
5.6%
49260
 
5.5%
58746
 
5.2%
38512
 
5.1%
28400
 
5.0%
67816
 
4.7%
77132
 
4.3%
136716
 
4.0%
86545
 
3.9%
Other values (65)51866
31.0%
ValueCountFrequency (%)
-143072
25.7%
04000
 
2.4%
19310
 
5.6%
28400
 
5.0%
38512
 
5.1%
49260
 
5.5%
58746
 
5.2%
67816
 
4.7%
77132
 
4.3%
86545
 
3.9%
ValueCountFrequency (%)
961
 
< 0.1%
931
 
< 0.1%
921
 
< 0.1%
882
< 0.1%
862
< 0.1%
841
 
< 0.1%
781
 
< 0.1%
681
 
< 0.1%
673
< 0.1%
661
 
< 0.1%

generic_make_model
Categorical

HIGH CARDINALITY

Distinct692
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
-1
47637 
FORD FIESTA
 
4850
VAUXHALL CORSA
 
4066
VAUXHALL ASTRA
 
3748
FORD FOCUS
 
3744
Other values (687)
103330 

Length

Max length30
Median length11
Mean length9.877138163
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAUDI Q5
2nd rowAUDI A1
3rd row-1
4th rowTOYOTA PRIUS
5th rowBMW 4 SERIES

Common Values

ValueCountFrequency (%)
-147637
28.5%
FORD FIESTA4850
 
2.9%
VAUXHALL CORSA4066
 
2.4%
VAUXHALL ASTRA3748
 
2.2%
FORD FOCUS3744
 
2.2%
VOLKSWAGEN GOLF3695
 
2.2%
BMW 3 SERIES2535
 
1.5%
VOLKSWAGEN POLO2248
 
1.3%
MERCEDES C CLASS1767
 
1.1%
TOYOTA YARIS1642
 
1.0%
Other values (682)91443
54.6%

Length

2022-02-22T15:10:27.421949image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
149279
 
15.6%
ford15568
 
4.9%
vauxhall12851
 
4.1%
volkswagen9790
 
3.1%
bmw7654
 
2.4%
mercedes7386
 
2.3%
honda6794
 
2.2%
series6510
 
2.1%
toyota6421
 
2.0%
audi5497
 
1.7%
Other values (734)188228
59.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

driver_imd_decile
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.904143391
Minimum-1
Maximum10
Zeros0
Zeros (%)0.0%
Negative31406
Negative (%)18.8%
Memory size1.3 MiB
2022-02-22T15:10:27.529716image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q11
median4
Q37
95-th percentile10
Maximum10
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.445328859
Coefficient of variation (CV)0.8824801023
Kurtosis-1.104427259
Mean3.904143391
Median Absolute Deviation (MAD)3
Skewness0.1028354394
Sum653456
Variance11.87029094
MonotonicityNot monotonic
2022-02-22T15:10:27.630732image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
-131406
18.8%
216706
10.0%
316306
9.7%
115378
9.2%
415131
9.0%
514460
8.6%
613655
8.2%
712267
 
7.3%
811597
 
6.9%
910935
 
6.5%
ValueCountFrequency (%)
-131406
18.8%
115378
9.2%
216706
10.0%
316306
9.7%
415131
9.0%
514460
8.6%
613655
8.2%
712267
 
7.3%
811597
 
6.9%
910935
 
6.5%
ValueCountFrequency (%)
109534
5.7%
910935
6.5%
811597
6.9%
712267
7.3%
613655
8.2%
514460
8.6%
415131
9.0%
316306
9.7%
216706
10.0%
115378
9.2%

driver_home_area_type
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
1
110099 
-1
31215 
3
14820 
2
11241 

Length

Max length2
Median length1
Mean length1.186497386
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row-1
4th row1
5th row-1

Common Values

ValueCountFrequency (%)
1110099
65.8%
-131215
 
18.6%
314820
 
8.9%
211241
 
6.7%

Length

2022-02-22T15:10:27.738868image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-22T15:10:27.819308image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
1141314
84.4%
314820
 
8.9%
211241
 
6.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Interactions

2022-02-22T15:10:15.221920image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:08:59.305599image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:09:03.129570image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:09:06.990940image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:09:10.826995image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:09:14.642375image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:09:18.511654image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:09:22.368351image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:09:26.269240image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:09:30.037773image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:09:33.885791image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:09:37.797134image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:09:41.661650image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:09:45.543948image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:09:49.288698image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:09:53.296242image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:09:57.223233image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:10:01.147278image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:10:05.895738image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:10:09.744293image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:10:15.446512image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:08:59.489246image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:09:03.317275image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:09:07.175271image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:09:11.018923image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:09:14.823747image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:09:18.694967image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:09:22.547336image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:09:26.449347image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:09:30.215761image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:09:34.072988image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:09:37.992427image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:09:41.849934image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:09:45.734874image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:09:49.488569image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:09:53.491201image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:09:57.437082image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:10:01.365436image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:10:06.062182image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:10:10.062975image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:10:15.617089image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:08:59.664098image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:09:03.499590image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:09:07.359261image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:09:11.201480image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:09:15.000510image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:09:18.878271image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:09:22.730968image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:09:26.629694image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:09:30.523868image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:09:34.266432image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:09:38.175316image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:09:42.025808image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:09:45.915078image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:09:49.676515image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:09:53.685767image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:09:57.625524image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:10:01.555156image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:10:06.224778image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:10:10.295771image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:10:15.792572image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:08:59.842709image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:09:03.675667image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:09:07.543611image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:09:11.414994image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:09:15.179026image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:09:19.065533image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:09:22.912207image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:09:26.814896image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:09:30.698936image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:09:34.457265image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:09:38.363311image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:09:42.216359image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:09:46.098328image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:09:49.860515image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:09:53.873154image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:09:57.810890image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:10:01.750793image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:10:06.391730image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:10:10.556195image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:10:16.025569image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:09:00.034681image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:09:03.862659image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:09:07.731130image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:09:11.606477image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:09:15.495806image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:09:19.249601image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:09:23.099980image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:09:26.989778image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:09:30.877768image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:09:34.630898image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:09:38.548512image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:09:42.424256image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:09:46.281935image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:09:50.051067image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:09:54.065656image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:09:57.998323image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:10:01.963699image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:10:06.562046image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:10:11.044287image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:10:16.261173image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:09:00.222936image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:09:04.045338image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:09:07.911387image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:09:11.791152image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:09:15.679431image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:09:19.431732image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:09:23.292438image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:09:27.179477image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:09:31.061574image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:09:34.814195image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:09:38.725709image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:09:42.601096image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:09:46.468219image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:09:50.376985image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:09:54.245618image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:09:58.184512image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:10:02.211619image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:10:06.722138image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:10:11.240524image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:10:16.489135image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:09:00.534522image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:09:04.229779image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:09:08.089501image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:09:11.973175image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:09:15.860546image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:09:19.607282image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:09:23.470719image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:09:27.372693image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:09:31.240761image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2022-02-22T15:09:44.627450image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:09:48.514227image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:09:52.504196image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:09:56.445082image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:10:00.402254image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:10:05.099936image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:10:08.785945image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:10:14.250926image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:10:18.808106image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:09:02.588423image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2022-02-22T15:09:33.328059image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:09:37.218824image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2022-02-22T15:10:05.279641image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:10:09.098470image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:10:14.550908image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2022-02-22T15:09:02.771011image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:09:06.631747image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:09:10.465788image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:09:14.278774image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2022-02-22T15:09:21.993837image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:09:25.900627image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:09:29.629291image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2022-02-22T15:09:02.954304image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:09:06.815618image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2022-02-22T15:09:18.335963image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2022-02-22T15:09:29.813548image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:09:33.710563image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:09:37.613280image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:09:41.474238image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:09:45.344018image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:09:49.093777image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:09:53.091343image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:09:57.028029image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:10:00.967324image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:10:05.729067image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:10:09.498435image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-22T15:10:14.963305image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2022-02-22T15:10:27.973313image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-02-22T15:10:28.632747image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-02-22T15:10:29.075255image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-02-22T15:10:29.459778image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-02-22T15:10:29.680719image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-02-22T15:10:19.581413image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-02-22T15:10:20.945685image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

accident_indexaccident_yearaccident_referencevehicle_referencevehicle_typetowing_and_articulationvehicle_manoeuvrevehicle_direction_fromvehicle_direction_tovehicle_location_restricted_lanejunction_locationskidding_and_overturninghit_object_in_carriagewayvehicle_leaving_carriagewayhit_object_off_carriagewayfirst_point_of_impactvehicle_left_hand_drivejourney_purpose_of_driversex_of_driverage_of_driverage_band_of_driverengine_capacity_ccpropulsion_codeage_of_vehiclegeneric_make_modeldriver_imd_deciledriver_home_area_type
020200102198082020102198081995150000004962326196826AUDI Q541
120200102204962020102204961904260200001121457139512AUDI A171
2202001022800520201022800519018-1-10000001163-1-1-1-1-1-1-1-1
3202001022800620201022800618018150000001111447179888TOYOTA PRIUS21
4202001022801120201022801119018379100001161204299324BMW 4 SERIES-1-1
52020010228012202010228012190181501010001161255139015AUDI A141
6202001022801420201022801419018510100001111417196924VOLVO V6031
7202001022801420201022801429318150100001163194214324MERCEDES C CLASS-1-1
8202001022801720201022801719018370600001111326299328JAGUAR XF SERIES81
920200102280172020102280172903370600000161266299320BMW X521

Last rows

accident_indexaccident_yearaccident_referencevehicle_referencevehicle_typetowing_and_articulationvehicle_manoeuvrevehicle_direction_fromvehicle_direction_tovehicle_location_restricted_lanejunction_locationskidding_and_overturninghit_object_in_carriagewayvehicle_leaving_carriagewayhit_object_off_carriagewayfirst_point_of_impactvehicle_left_hand_drivejourney_purpose_of_driversex_of_driverage_of_driverage_band_of_driverengine_capacity_ccpropulsion_codeage_of_vehiclegeneric_make_modeldriver_imd_deciledriver_home_area_type
1673652020991024209202099102420929013480800701151367196823VOLKSWAGEN PASSAT103
167366202099102452620209910245261980186200012003111437-1-1-1-131
1673672020991027064202099102706419016370300001111276-1-1-1-121
167368202099102706420209910270642105150600004151113-1-1-1-121
167369202099102957320209910295731901515100002132397159817NISSAN QASHQAI101
167370202099103029720209910302971907820600003111579196822AUDI A571
1673712020991030297202099103029725016620100001151387130112KTM 1290 SUPERDUKE92
1673722020991030900202099103090019078206000031626810199521BMW X351
16737320209910309002020991030900210186248000011617611-1-1-1-191
167374202099103257520209910325751901840001200216139799912FORD FOCUS71

Duplicate rows

Most frequently occurring

accident_yearvehicle_referencevehicle_typetowing_and_articulationvehicle_manoeuvrevehicle_direction_fromvehicle_direction_tovehicle_location_restricted_lanejunction_locationskidding_and_overturninghit_object_in_carriagewayvehicle_leaving_carriagewayhit_object_off_carriagewayfirst_point_of_impactvehicle_left_hand_drivejourney_purpose_of_driversex_of_driverage_of_driverage_band_of_driverengine_capacity_ccpropulsion_codeage_of_vehiclegeneric_make_modeldriver_imd_deciledriver_home_area_type# duplicates
1284202029099999999999999963-1-1-1-1-1-1-1-1198
1231202029099999999999991963-1-1-1-1-1-1-1-1189
97420202902000000002163-1-1-1-1-1-1-1-1105
97920202902000000003163-1-1-1-1-1-1-1-169
141020203902000000002163-1-1-1-1-1-1-1-166
471202019018150000001163-1-1-1-1-1-1-1-162
1273202029099999999999992963-1-1-1-1-1-1-1-158
97220202902000000001163-1-1-1-1-1-1-1-156
683202019018730000001163-1-1-1-1-1-1-1-153
547202019018370000001163-1-1-1-1-1-1-1-152